import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase





def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase				,     lowercase				):







				"""simple docstring"""



				if isinstance(lowercase				,     lowercase				):
								__lowercase					     =				np.full((len(lowercase				), sequence_length, 2)				,     lowercase				)
				else:
								__lowercase					     =				np.full((len(lowercase				), sequence_length)				,     lowercase				)

				for i, tensor in enumerate(lowercase				):
								if padding_side == "right":
												if isinstance(lowercase				,     lowercase				):
																__lowercase					     =				tensor[:sequence_length]
												else:
																__lowercase					     =				tensor[:sequence_length]
								else:
												if isinstance(lowercase				,     lowercase				):
																__lowercase					     =				tensor[:sequence_length]
												else:
																__lowercase					     =				tensor[:sequence_length]

				return out_tensor.tolist()





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				__lowercase					     =				ord(lowercase				)
				if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
								return True
				__lowercase					     =				unicodedata.category(lowercase				)
				if cat.startswith('''P'''				):
								return True
				return False




@dataclass
class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					PreTrainedTokenizerBase
			__a    :					Union[bool, str, PaddingStrategy]     					=		True
			__a    :					Optional[int]     					=		None
			__a    :					Optional[int]     					=		None
			__a    :					int     					=		-100
			__a    :					str     					=		"pt"





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__		)	->   List[str]:

							'''simple docstring'''


							import torch

							__lowercase					     =				'''label''' if '''label''' in features[0].keys() else '''labels'''
							__lowercase					     =				[feature[label_name] for feature in features] if label_name in features[0].keys() else None
							__lowercase					     =				self.tokenizer.pad(
							    lowerCAmelCase__ ,  padding=self.padding ,  max_length=self.max_length ,  pad_to_multiple_of=self.pad_to_multiple_of ,  return_tensors='''pt''' if labels is None else None ,  )

							if labels is None:
											return batch

							__lowercase					     =				torch.tensor(batch['''entity_ids''']		).shape[1]
							__lowercase					     =				self.tokenizer.padding_side
							if padding_side == "right":
											__lowercase					     =				[
											    list(lowerCAmelCase__		) + [self.label_pad_token_id] * (sequence_length - len(lowerCAmelCase__		)) for label in labels
											]
							else:
											__lowercase					     =				[
											    [self.label_pad_token_id] * (sequence_length - len(lowerCAmelCase__		)) + list(lowerCAmelCase__		) for label in labels
											]

							__lowercase					     =				[feature['''ner_tags'''] for feature in features]
							__lowercase					     =				padding_tensor(lowerCAmelCase__ ,  -1 ,  lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				[feature['''original_entity_spans'''] for feature in features]
							__lowercase					     =				padding_tensor(lowerCAmelCase__ ,  (-1, -1) ,  lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				{k: torch.tensor(lowerCAmelCase__ ,  dtype=torch.intaa		) for k, v in batch.items()}

							return batch
import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class   _UpperCamelCase (		_UpperCAmelCase		,_UpperCAmelCase					):


			"""simple docstring"""






			@register_to_config
			def __init__(  self ,  *,
			    lowerCAmelCase__ = 4 ,  lowerCAmelCase__ = 7_68 ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  )	->   Dict:

							'''simple docstring'''


							super().__init__()

							__lowercase					     =				nn.Parameter(torch.zeros(lowerCAmelCase__		)		)

							# parameters for additional clip time embeddings
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)

							# parameters for encoder hidden states
							__lowercase					     =				clip_extra_context_tokens
							__lowercase					     =				nn.Linear(
							    lowerCAmelCase__ ,  self.clip_extra_context_tokens * cross_attention_dim		)
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				nn.LayerNorm(lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  *, lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   List[str]:

							'''simple docstring'''


							if do_classifier_free_guidance:
											# Add the classifier free guidance embeddings to the image embeddings
											__lowercase					     =				image_embeddings.shape[0]
											__lowercase					     =				self.learned_classifier_free_guidance_embeddings.unsqueeze(0		)
											__lowercase					     =				classifier_free_guidance_embeddings.expand(
											    lowerCAmelCase__ ,  -1		)
											__lowercase					     =				torch.cat([classifier_free_guidance_embeddings, image_embeddings] ,  dim=0		)

							# The image embeddings batch size and the text embeddings batch size are equal
							assert image_embeddings.shape[0] == prompt_embeds.shape[0]

							__lowercase					     =				prompt_embeds.shape[0]

							# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
							# adding CLIP embeddings to the existing timestep embedding, ...
							__lowercase					     =				self.embedding_proj(lowerCAmelCase__		)
							__lowercase					     =				self.clip_image_embeddings_project_to_time_embeddings(lowerCAmelCase__		)
							__lowercase					     =				time_projected_image_embeddings + time_projected_prompt_embeds

							# ... and by projecting CLIP embeddings into four
							# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
							__lowercase					     =				self.clip_extra_context_tokens_proj(lowerCAmelCase__		)
							__lowercase					     =				clip_extra_context_tokens.reshape(lowerCAmelCase__ ,  -1 ,  self.clip_extra_context_tokens		)
							__lowercase					     =				clip_extra_context_tokens.permute(0 ,  2 ,  1		)

							__lowercase					     =				self.encoder_hidden_states_proj(lowerCAmelCase__		)
							__lowercase					     =				self.text_encoder_hidden_states_norm(lowerCAmelCase__		)
							__lowercase					     =				torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] ,  dim=1		)

							return text_encoder_hidden_states, additive_clip_time_embeddings